Integrative analyses of serum proteome and metabolome uncovers novel biomarkers for disease activity monitoring and clinical diagnoses for systemic lupus erythematosus

Objective: To systematically determine the serum protein and metabolite expression characteristics, to identify novel biomarkers for disease activity monitoring and clinical diagnoses in patients with systemic lupus erythematosus (SLE).<br> Methods: Serum samples from 121 SLE patients and 106 healthy controls were conducted to proteomics and metabolomics analyses. Disease activity score (SLEDAI) was compared with protein and metabolite expression and clinical data. Random forest machine learning model was performed to identify biomarkers for SLE classification. The clinical utility of the biomarkers was further validated in an independent patient cohort.<br> Results: Screening of the serum proteome and metabolome identified 90 proteins and 76 metabolites significantly changed in SLE patients. Pathway analyses of these molecules revealed SLE related alterations, including immune response, endocytosis and lipid metabolism. Several apolipoproteins and the metabolite arachidonic acid were significantly associated with disease activity. Besides, except some well-known biomarkers, novel molecules such as the protein Apolipoprotein A-IV (APOA4) and the metabolites LysoPC(16:0), punicic acid and stearidonic acid were correlated with renal function in SLE condition. Random forest model by using the significantly changed proteins and metabolites identified 11 proteins and 5 metabolites as potential biomarkers. Among them, 9 proteins (AUC=0.895) and 5 metabolites (AUC=0.902) were validated in an independent patient cohort, which showed good performance for SLE classification.研究目标：系统性明确系统性红斑狼疮（systemic lupus erythematosus, SLE）患者的血清蛋白质与代谢物表达特征，筛选可用于该疾病病情活动监测与临床诊断的新型生物标志物。 研究方法：收集121例SLE患者与106例健康对照者的血清样本，开展蛋白质组学与代谢组学分析；将系统性红斑狼疮疾病活动度评分（SLEDAI）与蛋白质、代谢物表达水平及临床资料进行关联分析；采用随机森林机器学习模型筛选用于SLE分类诊断的生物标志物，并在独立患者队列中进一步验证该类生物标志物的临床应用价值。 结果：通过血清蛋白质组与代谢组筛选，发现SLE患者体内存在90种差异表达蛋白质与76种差异表达代谢物。对上述差异分子的通路富集分析显示，其涉及与SLE相关的多种生物学过程，包括免疫应答、内吞作用与脂质代谢。部分载脂蛋白及代谢物花生四烯酸与疾病活动度显著相关。此外，除部分已知生物标志物外，新型分子如蛋白质载脂蛋白A-IV（APOA4）以及代谢物溶血磷脂酰胆碱(16:0)、石榴酸与十八碳四烯酸均与SLE患者的肾功能显著相关。基于差异表达蛋白质与代谢物构建的随机森林模型，筛选出11种蛋白质与5种代谢物作为潜在生物标志物。其中9种蛋白质（AUC=0.895）与5种代谢物（AUC=0.902）在独立患者队列中得到验证，其用于SLE分类诊断的性能优异。